LlamaParse with Upstash Vector

pip install llama-index upstash-vector llama-index-vector-stores-upstash python-dotenv

UPSTASH_VECTOR_REST_URL=your_upstash_url
UPSTASH_VECTOR_REST_TOKEN=your_upstash_token
LLAMA_CLOUD_API_KEY=your_llama_cloud_api_key

from llama_parse import LlamaParse
from llama_index.core import SimpleDirectoryReader

# Initialize the parser
parser = LlamaParse(result_type="markdown")

# Parse a document
file_extractor = {".txt": parser}
documents = SimpleDirectoryReader(
    input_files=["./documents/global_warming.txt"],
    file_extractor=file_extractor
).load_data()

from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.upstash import UpstashVectorStore
from llama_index.core import StorageContext
from dotenv import load_dotenv
import os

# Load environment variables
load_dotenv()

# Set up Upstash Vector Store
vector_store = UpstashVectorStore(
    url=os.getenv("UPSTASH_VECTOR_REST_URL"),
    token=os.getenv("UPSTASH_VECTOR_REST_TOKEN")
)

# Create storage context and index the parsed document
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# Perform a query
query_engine = index.as_query_engine()
response = query_engine.query("What is the main topic discussed in the document?")